Modelling clusters of corporate defaults: Regime‐switching models significantly reduce the contagion source

Geir Drage Berentsen, Jan Bulla, Antonello Maruotti and Bård Støve

Journal of the Royal Statistical Society Series C, 2022, vol. 71, issue 3, 698-722

Abstract: In this paper, we report robust evidence that the process of corporate defaults is time‐dependent and can be modelled by extending an autoregressive count time series model class via the introduction of regime‐switching. That is, some of the parameters of the model depend on the regime of an unobserved Markov chain, capturing the model changes during clusters observed for count time series in corporate defaults. Thus, the process of corporate defaults is more dynamic than previously believed. Moreover, the contagion effect—that current defaults affect the probability of other firms defaulting in the future—is reduced compared to models without regime‐switching, and is only present in one regime. A two‐regime model drives the counts of monthly corporate defaults in the United States. To estimate the model, we introduce a novel quasi‐maximum likelihood estimator by adapting the extended Hamilton–Gray algorithm for the Poisson autoregressive model.

Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1111/rssc.12551

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:jorssc:v:71:y:2022:i:3:p:698-722

Ordering information: This journal article can be ordered from
http://ordering.onli ... 1111/(ISSN)1467-9876

Access Statistics for this article

Journal of the Royal Statistical Society Series C is currently edited by R. Chandler and P. W. F. Smith

More articles in Journal of the Royal Statistical Society Series C from Royal Statistical Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().